MétaCan
Menu
Back to cohort
Record W4386837067 · doi:10.3390/curroncol30090618

Reduced Risk of All-Cause, Cancer-, and Cardiovascular Disease-Related Mortality among Patients with Primary Malignant Cardiac Tumors Receiving Chemotherapy in the United States

2023· article· en· W4386837067 on OpenAlexvenueno aff
Duke Appiah, Carina R. Goodart, Grishma K. Kothari, Imo Ebong, Chike C. Nwabuo

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHazard ratioInternal medicineChemotherapyCancerEpidemiologyProportional hazards modelDiseaseConfidence intervalSurgeryOncology

Abstract

fetched live from OpenAlex

Primary malignant cardiac tumors (PMCTs) are rare but lethal neoplasms. There are limited evidence-based treatment guidelines for PMCTs. We evaluated the relation of chemotherapy with mortality outcomes in patients with PMCTs in the United States. Data were from patients aged ≥ 20 years from the Surveillance, Epidemiology, and End Results program who were diagnosed with PMCTs from 2000 to 2020. Cox regression, competing risk, and propensity score analyses were performed to estimate hazard ratios (HR) and confidence intervals (CI). About 53% of the 563 patients with PMCTs received chemotherapy as the first course of treatment. During a mean follow-up of 24.7 months (median: 10), 458 deaths occurred with 81.7% and 9.4% due to cancer and cardiovascular disease (CVD), respectively. In models adjusted for sociodemographic and clinico-pathophysiological factors including histology, receipt of chemotherapy was associated with low risk for all-cause (HR: 0.56, 95%CI: 0.45-0.69), cancer (HR: 0.63, 95%CI: 0.50-0.80) and CVD mortality (HR: 0.27, 95%CI: 0.12-0.58). Patients who had both chemotherapy and surgery had the lowest risk for all-cause and cancer mortality. This study suggests that the subpopulations of patients with PMCTs who receive chemotherapy may have better prognosis than those who do not receive this therapy regardless of histology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.329
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCurrent OncologySame topicCardiac tumors and thrombiFrench-language works237,207